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Journal of Chinese StudiesZhengge and Renjun Journal of Chinese Studies (2016) 1:2 DOI 10.1186/s40853-016-0004-x
RESEARCH ARTICLE Open Access
Productivity growth in China’s largeindustrial firms: patterns, causes, andimplications
TU Zhengge* and Shen Renjun
* Correspondence:[email protected] of Economics and BusinessAdministration, Central ChinaNormal University, 152 Luoyu Road,Wuhan 430079, China
Background: China’s economy has experienced a high-rate growth since the reformand opening up in 1978. However, some macro-statistics indicated that China’seconomy grows with a low efficiency. In fact, these conclusions based on macro-statistics and the performance of the economy as a whole cannot reflect the multi-level, dynamic and, therefore, complicated situation of China. By using firm-leveldata, and the stochastic frontier production model, this paper makes an investigationon the changing trends of TFP of China’s LMIE between 1995 and 2002, and thedecomposition of factors of growth.
Results: Main findings include: (1) the weighted average of the annual growth ofTFP in China’s large and medium sized industrial enterprises was as high as 6.8 %with a rising trend during 1996-2002; (2) the contribution to TFP growth by thefactor of Frontier Technology Progress reached as much as 14 percentage points perannum on average; (3) the decline in Technical Efficiency (Relative to the Frontier)reduced the TFP growth by 7.1 percentage points per annum on average; (4)Allocative Efficiency contributed on average only 0.02 percentage points per annumto the TFP growth and Scale Dis-Economy slowed the growth of TFP by 0.33percentage points per annum.
Conclusions: The results show that at the turn of the century, the most importantpart of China’s industry was in the middle of an industrial productivity revolutiondriven by both Frontier Technological Progress and the great potential of technicalefficiency of lagging enterprises. The revolution is driven by increased competition,privatization, foreign investment, and business expansion.
Keywords: Total factor productivity, Frontier technology progress, Technicalefficiency, Scale economy, Allocative efficiency, Large and medium-sized industrialenterprises (LMIE)
BackgroundOver the two decades since its opening up to the world, the increase in Gross Domes-
tic Product (GDP) of China keeps at a level between 8 and 9 % per year. However,
many economists point out that the high growth rate of China could not be attributed
to the improvement of productivity, but to the high saving rate and massive capital in-
put, including the inflow of foreign capital. Furthermore, some macro-statistics also
indicated that China’s economy grows with a low efficiency. For example, according to
The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Internationalicense (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,rovided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, andndicate if changes were made.
Notes: The non-constraint value of log likelihood LR = -196395. The five null Hypothesis (HB0B) are all rejected at thesignificant level of 1 %.athe critical value for this test involving γ = 0 comes from Table 1 of Kodde, Palm (1986, p. 1246)
Zhengge and Renjun Journal of Chinese Studies (2016) 1:2 Page 8 of 16
For space limitation, the tests of hypotheses are given for total sample more than by
industry. Traditional research of productivity used average production function (the
elasticity of output with respect to factors is average) to measure productivity, but the
production function ignored the influence due to technical inefficiency on production.
If the technical inefficiency does not exist, the estimation becomes a simple OLS
estimation. There is no stochastic frontier parameter in the model, and the stochastic
frontier model becomes an average production model. Therefore, the null hypothesis
H0 : γ = μ = η = 0 is examined. If the null hypothesis holds, and then technical ineffi-
ciency does not exist. The resulting statistics rejects the null hypothesis at significant
level of α = 1 %, which suggests that compared with the stochastic frontier production
function, the average production function is an inadequate representation of LMIE in
China, and underestimates the actual frontier because of technical inefficiency effects.
The second null hypothesis, that technical inefficiency is time-invariant (H0: η = 0), is
also rejected at the 1 % significant level for the total sample. This means the technical
efficiency in China’s LMIE during year 1995–2002 is time varying, given the stochastic
frontier model defined by equation (8).
The third null hypothesis, that there is no technical progress (H0 : αΤ = βTT = βTL= βTK = 0), and the fourth null hypothesis, that technical progress is neutral (H0 : βΤΚ= βΤL = 0), are both rejected at the 1 % significant level for the total sample. This
implies that non-neutral technical progress in China’s LMIE exists.
The last null hypothesis, that the technology in China’s LMIE is a Cobb-Douglas (H0 :
βΚΚ = βLL = βLΚ = βTT = 0), is also rejected at the 1 % significant level for the total
sample. Thus, the Cobb-Douglas production function is not an adequate specification
for China’s LMIE, given the assumptions of the trans log stochastic frontier production
function model.
All results of the test show that the trans log stochastic frontier production model
with non-neutral technology and time-varying technical inefficiency is really better than
other models.
ResultsMain empirical results
Stochastic frontier production models are estimated individually for each of the 37
two-digital industries, and then the TFP growth, FTP, change rate of TE, AE and SE are
calculated for all the industries during the period 1995–2002.
Zhengge and Renjun Journal of Chinese Studies (2016) 1:2 Page 9 of 16
Dynamics of TFP growth
How to calculate the growth rate of TFP by using the growth accounting method is
already described above. TFP growth is made out with the industry growth rate of
added-value, labor and net value of fixed asset. Deflating the price factor, the average
annual growth rate of industry added-value for all China’s LMIE reaches as much as
11.5 % during the years 1996–2002, which was close to the growth rate for all industrial
firms announced by the National Bureau of Statistics. Capital stock’s average annual
growth rate come up as much as 12.3 %, and the total number of labor employed
decreases at a rate of 4.4 % yearly. The industry added-value was taken as the weight in
calculating the average values for all variables involved.
The LMIE’ rate of increment of TFP during the years 1996–2002 was an average of
6.8 % per year. It decreased by 4.3 % in the year 1996, and 0.7 % in 1997, and then it
grows at an accelerated speed. And it was 3.0, 7.5, 11.2, 8.2, and 14.0 %, respectively,
during the 1998–2002 periods.
Among all the industries during these years, the five industries which had the most
rapid average annual rate of increment of TFP are: transport equipment manufacturing,
with 17.8 %, instrument manufacturing, with 16.0 %, Print, with 13.8 %, metal manufac-
turing, with 13.6 %, medicine manufacturing, with 12.4 %; and the five slowest indus-
tries are: petroleum processing, with -4.6 %, electric power industry, with -4.1 %, tap
water industry, with -4.0 %, chemical fiber & textile manufacturing, with 2.1 %, petrol-
eum & natural gas industry, with 2.3 %. It’s shown that the industries facing high extent
of opening-up and intense competition have a high rate of increment of TFP. In the op-
posite, the industries with high degree of monopoly and low degree of opening-up have
a low rate of increment of TFP, even a negative one.
In general, LMIE’ increment of productivity in China decreased initially and then in-
creased, and was increasing yearly as a whole during the years 1996–2002. The large-
scale investment in the years 1995–1997 did not brought high benefit, which was
affected by the financial crisis and macro-control, and the TFP decreased in both the
two years. After 1998, the productivity increased rapidly as well as the whole Asian
economy’s recovery.
Frontier technological progress
FTP means the extra rate of increment of the frontier output through the technical im-
provement without adding any input, and it directly affects the improvement of the
TFP. Viewing from the mathematic formula representing the contribution of the FTP
to the productivity, this contribution of FTP is not related to the level of the factor in-
put, but related to the time t.
On the whole, the FTP during the years 1995–2002 sped up, and FTP of China’s
LMIE in 37 industries made an average annual contribution of 14 % to the TFP. FTP
made TFP grow fast mainly after the year 1998. FTP made TFP grow by 9.0 % in the
year 1998, 12.3 % in 1999, 15.3 % in 2000, 18.9 % in 2001, and 22.6 % in 2002. The de-
tailed data are available in the appendix.
The five industries, whose FTP made the greatest contribution to their TFP, were:
telecommunications facilities industry, with 26.3 %, ferrous metal smelting processing,
with 24.4 %, transport equipment manufacturing, 23.0 %, medicine manufacturing,
22.7 %, food manufacturing, 21.5 %. And the five industries which had the slowest
Zhengge and Renjun Journal of Chinese Studies (2016) 1:2 Page 10 of 16
improvement of FTP to TFP are: gas production and supply industry, with 2.85 %, tap
water industry, with 2.4 %, petroleum processing, with 1.4 %, petroleum & natural gas,
0.98 %, electric power industry, 0.17 %.
How to understand the effect of FTP in these China’s LMIE? The stochastic frontier
production function represents the industrial best technical structure in terms of the
input and output, so the contribution made by the FTP here is the increment of the
TFP in the best performance firms, and the figure is far higher than that of the average
firms that are departures from the frontier and has technical inefficiency relative to the
frontier, inefficient AE and diseconomies of scale. While in actual fact, many enter-
prises are under the frontier.
What is the real driving force behind the FTP? In the stochastic frontier production
function, time t is a variable used for the weight of the technical change, and time is
also a variable that can hold everything. This means the technology used here has a
very broad content, it includes not only direct technical factors, such as advanced
technologies, patents, technical innovation, high-tech equipment and talented
personnel, but also economic circles, but also non-technical factors such as economy,
society, vicissitude of legal institutions, which refers to the development of private en-
terprises, the reform of state-owned enterprises, reform of tax system, foreign invest-
ment, and joining WTO, etc. These factors have a deep impact on the output and
productivity. Our main purpose here is to verify the huge contribution made by FTP to
the increment of China’s enterprises’ productivity, while it’s hard to analyze in depth
the factors behind the FTP in such a length-limited article. Still, four factors are
concluded for further statistical analysis.
First, the intense competitions among the enterprises in the same industry are the
driving force for FTP. As the reform toward the market economy progresses in China,
the whole economy changes from the shortage economy to excessive supply, and the
tariff protection gradually decreases, while the market competition becomes more
intense. All of these make the enterprises adopting advanced techniques and invest
more money on technologies, so as to keep a favorable position in the market competi-
tion. Intensive competitions advance greatly the industrial FTP. The 37 industrial
sectors with slow FTP such as gas, tap water, electric power, petroleum processing and
exploitation, are all highly monopolized by the state, and are all lack of competitions.
While the industries with FTP having significant contribution to the growth of TFP,
such as telecommunications facilities, food production, food processing, pharmacy, are
all with a high degree of opening-up and competition.
Second, globalization and foreign direct investment are the sources of FTP growth.
Accession to WTO provides China’s enterprises a broad market prospect, thus, more
advanced technologies and a better product quality are required. At the same time, the
foreign manufacturers’ entry into China’s market has accelerated the pace of technology
adaptation and update. Of all the LMIE, the number of foreign-invested enterprises
reached 2935 in the year 2002 which is about 3 times the number in the year 1995,
which were about 1,000. And the number of those invested by manufacturers from
Taiwan, Hong Kong and Macao was 2495 in 2002, compared with 936 in the year
1995.
Third, the reform of ownership is an internal driving force of FTP. Clear property
rights ensure the rewards to technical innovation and this is an interior driving force of
Zhengge and Renjun Journal of Chinese Studies (2016) 1:2 Page 11 of 16
FTP. The structure of ownership of the LMIE has changed a lot in the years during
1995–2002. The number of private enterprises reached to 1302 in 2002, which was only
5 in 1995; the number of mixed-owned enterprises reached 6135 from 1233; the pro-
portion of foreign-invested enterprises including those invested by Taiwan, Hong Kong
and Macao increased about two times than that in 1995, while the state-owned enter-
prises’ number decreased from 15,361 to 7,215, and the number of collective-owned
ones from 4008 to 2138. These are of the institution reform factor of the increment of
FTP.
Fourth, the expansion of the economy brings a better external environment for FTP
improvement. The recovery of economy will bring huge demand,enabling enterprises
to make huge output with limited input thereby improving productivity significantly. In
contrast, in a contracted economy, the demand decreases dramatically, and the output
will reduce consequently so does the employment rate and assets, especially the fixed
assets. As a result, the productivity will remarkably decrease. During the years 1996 to
2002, the growth rate of China’s GDP kept at about 8 %, and the whole society’s fixed
assets investment ratio kept at above 10 % except for two years. So, the expansion
period of economy in the years 1996 to 2002 undoubtedly made a favorable external
environment for FTP.
Technical efficiency (TE) and its rate of change
Technical efficiency relative to the frontier is the ratio of the enterprise’s actual output
to the frontier output. The rate of change of Technical efficiency relative to the frontier_TE is one important factor which makes the productivity change. With the results of
the estimation on the 37 industries in the stochastic frontier production function
model, each industry’s TE and _TE are obtained. The average industry value of TE is
weighed by industry added-value, too.
First the average industry value of TE is generated according to the estimation of
every enterprise’s TE followed by acquisition of the average TE of all the LMIE. The re-
sults exhibit that the average TE of all the LMIE was just 31 % in the years 1996 to
2002, which is the average ratio of real output to the optimal stochastic output level.
TE in the years 1996 to 2002 is 32, 32, 32, 31, 30, 30 and 29 % respectively, which
means that TE is below one third on average and has been decreasing.
In view of different industries, the industries with the highest average annual TE in
the years 1996 to 2002 are: tobacco industry with 55 %, ferrous metals mining and
dressing with 51 %, paper manufacturing 46 %, nonferrous minerals smelting process-
ing industry with 45 %, textile industry with 44 %; and the industries with the lowest
average annual TE are: telecommunications facilities manufacturing with 12 %, instru-
ments industry with 15 %, electric machine industry with 16 %, gas produce and supply
industry with 17 %, arts & crafts and other manufacturing industry 18 %. As for TE dis-
tribution, traditional industries (such as mineral, textile, paper manufacturing), high
monopolized industries (such as tobacco industry) have higher TE than machine, elec-
tronic, and instruments industries. How to explain it?
It’s seen from the definition of TE and the calculation method of the average industry
TE that an enterprise’s technical efficiency relative to the frontier level depends on the
industrial frontier technical level, viz. the benchmarks level. Production frontier is the
set of the maximum output which is corresponding to various sets of input factors. If
Zhengge and Renjun Journal of Chinese Studies (2016) 1:2 Page 12 of 16
the degree of the industrial technical creation and introduction has been enhanced,
then the industrial frontier output level will greatly increase and the frontier production
will greatly move up. If the technical innovation and adoption are limited to a few en-
terprises, and most firms are far from the production frontier, the average industry
technical efficiency will not be high; on the contrary, if the technical innovation and
FTP are not very remarkable, like some traditional industries, which has mature and
stable technologies and whose production frontier does not change much, and whose
technologies are pervasive in the industry, then most enterprise in the industry will
have an output approaching the production frontier, and these industries have a higher
TE compared with the new industries.
In addition, the weight influences the industrial average TE. If the very large enter-
prises in the industry have very low TE, then the usage of relative size firms as a weight
will decrease the average industry TE. So the industrial FTP is considered as well as the
industry structure to explain the industrial TE level. Therefore, it’s not difficult to
understand that some industry with a high technique does not have high TE.
With China’s reform and opening-up, huge Multinational Corporations’ entry will un-
doubtedly increase the difference of technology level among enterprises in each indus-
try as well as technical efficiency among different enterprises, and decrease the
industrial average TE level. On the other hand, the increasing technical differences
among enterprises in different regions are another reason for the low TE level. It is an
important character of our LMIE nowadays that there are huge differences of TE. And
this reflects a problem that many enterprises face the pressure of market competition.
But in view of the whole economy’s development, the differences will be the potential
sources of enhancing the overall technology level. That’s another reason why we believe
that China’s industrial productivity is coming into a modernization reform.
In the analytic framework of frontier production function, we are concerned more
about the change of TE’s influence to the productivity. According to the frontier pro-
duction function model, the change in TE determines the change in one firm’s enter-
prise’s technical efficiency.
This article aims to analyze the TE change in terms of the industries’ average trend.
The change in TE and its influence on productivity are expressed by the weighted aver-
age of enterprises’ TE. The decline in TE of 37 industrial sectors lessened the product-
ivity by 7.1 percentage points per year during 1996–2002. From -6.2 % in 1996 to
-7.9 % in 2002, the average TE was obviously characterized by a sharp falling trend. Of
these 37 industrial sectors, only the tap water industries’ TE got slightly better with an
increase of 0.7 %; the decrease of TE was 0.4 % per year in power industry, 2.3 % in
non-metal mining industry, 2.5 % in tobacco industry, 2.6 % in gas production industry;
the top five industries with the greatest decrease of TE were chemical material indus-
tries with 11.1 % per year, food production industries with 11.1 %, beverage industry
with 11.6 %, rubber industry with 12.1 % and electronics and telecommunication equip-
ment manufacturing industry with 15 %.
Monopolistic industries were surprisingly characterized by low decrease in TE, while
intensively competitive industries with large sum of foreign investment were character-
ized by rapid decrease in TE, such as food processing industry, food producing indus-
try, electronics and telecommunication industry. What do the dynamic characteristics
imply? Electronics and telecommunication industry, food industry are characterized by
Zhengge and Renjun Journal of Chinese Studies (2016) 1:2 Page 13 of 16
an increasingly intensive competition; new technology, manage experiences and tech-
niques learned from foreign enterprises have promoted the industrial production fron-
tier at the same time broadening the TE gaps between the frontier technology and a
firm’s actual production, which may decrease the average TE relative to frontier. From
a policy perspective, the low TE implies that it is possible to increase TE by market
choosing and emulating along with learning from, catching up with and helping each
other, which means that the potential of productivity revolution exists. The crucial fac-
tors which determine the gap of TE are further elaborated in terms of the ownership
structure change, technological spillover, and industrial competition situation, firm’s
size in the sixth part.
Allocative efficiency
Optimizing allocation of production factors will bring about higher productivity. The cost
and marginal output of production factors varies across firms in every industry. If a factor
is with higher cost but lower marginal income, the rapid growth of this factor in an indus-
try will consequently cause the decline in productivity of enterprises and of the industry
on average; conversely, the decrease of this factor will promote productivity, which is de-
fined as the allocative efficiency of resources. Free flow of labor and capital makes the rise
of resource allocative efficiency possible. From the macro-economy perspective, along
with the deepening of the Chinese market-oriented economy reform, the rise of the social
resources allocative efficiency became the main contribution factor to the rapid develop-
ment of Chinese economy. The research of this article mainly targeted at the enterprises
in an industry and analyzed the allocative efficiency of input factors from the enterprises
aspect. Inter industry comparison is not studied in this paper.
On average, the growth of productivity caused by the rise of the AE, which contrib-
uted to TFP only by 0.14 % per year during 1996–2002, was not significant in all 37 in-
dustrial sectors. During this period, the contributions to productivity by AE are 0.15,
0.34, 0.05, 0.26, 0.16, -0.14 and 0.15 percentage points a year on average, respectively.
From the dimension of industries, during 1996–2002 the rises of productivity due to
the improvement of AE were respectively 4.44 % per year on average in petroleum and
natural-gas extraction industry, 3.97 % in printing industry, 3.0 % in ferrous mining in-
dustry, 2.063 % in gas production industry and 1.99 % in instruments industry; the fall
of productivity caused by ineffective allocation of resources are respectively 1.52 % per
year on average in paper-making industry, 1.75 % in ferrous melting industry, 1.87 % in
beverage industry, and 2.23%s in petroleum processing industry, 2.38%s in power in-
dustry. In most of other industries, the contribution of resource AE to the productivity
was extremely low. Moreover, AE has contributed almost zero to TFP among all the
LMIEs. Is this a good phenomenon or an ill omen? How to explain?
If the capital and labor flow freely, production factors will seek the highest return and
enterprises will also maximize their profit by selecting the optimal combination of pro-
duction factors. Under the circumstance of perfect competition, the equilibrium condition
for maximum profit is that the marginal cost of a factor equals to its price (cost). Under
this scenario, the contribution of AE to the productivity will come to zero. In reality, as
far as a certain enterprise or industries is concerned, the equilibrium condition is hardly
satisfied and the AE may be positive or negative for varies reasons,. However, when we
calculating the average contribution of all the enterprises in an industry with industrial
value added as the weight, the positive and negative values will offset each other, which
Zhengge and Renjun Journal of Chinese Studies (2016) 1:2 Page 14 of 16
imply that the growth of productivity attributed to the efficiency of resource allocation
would be nearly zero. This result tells us that the marketization of Chinese economy has
been improving greatly and the function of market in allocating resources has been
enforced; overall on average, the potentiality to promote the productivity by optimizing
the efficiency of resource allocation may not be great, nevertheless, at the firm level, as to
some industries and enterprises, there is still potential to promote the AE.
Return to scale and scale economy
The industrial frontier production function estimated the return to scale and the scale
efficiency in terms of the mean value of industrial input factors, therefore, the return to
scale and scale efficiency we discussed represented the average situation of an industry.
The index of return to scale is the sum of the elasticity of factors in-put (εl, εk). If the
RTS is more than one, it means that after controlling the technical progress and the
technology inefficiency, an enterprise still enjoys the scale efficiency by enlarging the
scale which means that the ratio of production growth is higher than that of in-put fac-
tor growth. The calculation showed that the return to scale was on average 0.903 for
LMIEs in all the 37 industries. In these 37 industries, only the tobacco industry enjoyed
the return to scale of 1.42 on average and the tap water enjoyed 1.033, just more than
one, which illustrated the efficiency of larger enterprises was higher given the marginal
efficiency of capital and labor (that is the production elasticity of factor input). The re-
turn to scale in garment industry, petroleum and natural-gas extraction industry and
petroleum processing was near to one. However, the return of scale in other industries
were much less than one, with 0.739 in nonferrous melting industry, 0.731 in chemical
material and products industry, 0.715 in instrument industry, 0.708 in timber industry,
and 0.657 in gas product and supply industry, respectively, which indicated that the ef-
ficiency was lower in larger enterprises given the production elasticity of factors. The
ownership of these large and super large enterprises mainly belonged to the state, sug-
gesting that the inefficiency of state-owned enterprises may be one of the reasons why
the return to scale was small than one in lots of industries.
The contribution of scale economy to productivity depends on two factors: the index
of return to scale and the composite index of inputs growth. If the return to scale
(RTS) is constant, RTS equal to one, change of the scale has no effect on productivity.
However, the variation of scale may have an effect on productivity; no matter the return
to scale is more than one or less than one. The ultimate outcome depends on the index
of the composite index of inputs growth. When the RTS is larger than one, the expan-
sion of scale will increase the productivity and the reduction of scale will decrease the
productivity; when the RTS is less than one, the results are the opposite.
From the industry point of view, during 1996–2002 the top-five industries with average
annual improvements of productivity resulted from the scale economy were respectively
tobacco industry with 1.07 %, special equipment industry with 0.59 %, nonferrous mining
industry with 0.57 %, instrument industry with 0.46 %, and non-metal mining industry
with 0.39 %; the five industry whose deduction of productivity resulted from scale effi-
ciency were plastic making industry with 2.02 percentage points, paper making industry
with 2.06 %, furniture industry with 2.60 % percentage points, gas product and supply in-
dustry with 3.355 %, and timber industry with 3.53 %, respectively.
In general, the deduction of productivity owing to the scale dis-economy in China’s
industrial sector was 0.33 % per year on average, which was insignificant compared
Zhengge and Renjun Journal of Chinese Studies (2016) 1:2 Page 15 of 16
with the influence of frontier technology progress and TE improvement on productiv-
ity. The effect of scale economy on productivity were respectively -0.58, -0.63, -0.07,
-0.04, 0.06, -0.53 and -0.52 during 1996–2002. However, on the micro level, there were
some enterprises in certain industry with the potential of improving productivity by
adjusting their scale. A thorough analysis of the factor allocation and scale economy on
micro level will be presented in another article.
DiscussionTaking a comprehensive view of the five aspects analyzed above, the conclusion are drawn
that along with the deepening of China’s market-oriented economic reform and improve-
ment of the economic environment, the total factor productivity of China’s LMIE was grow-
ing continuously. Frontier technology progress was the main source of the productivity
increase. However, the significant progress of production technology frontier consequently
broadened the gap of technology efficiency between the frontier technology and the actual
output. This gradually broadened efficiency gap between enterprises greatly obstructed the
productivity rise of the whole industries. But it would be the potential driving force of prod-
uctivity growth in the future. Using the stochastic frontier model for estimating the data of
the LMIE during 1995–2002, these conclusions are made: (1) the weighted average of the an-
nual growth of TFP was as high as 6.8 % with a rising trend; (2) the contribution to TFP by
frontier technology progress reached as much as 14 % per year; (3) the broadened gap of
technology efficiency (relative to frontier) between enterprises reduced the growth of TFP by
7.1 % year on average; (4) Scale dis-economy slowed the growth of TFP by 0.33 % per year
on average;(5) AE contributed on average only 0.02 % to the growth of TFP per year. The er-
rors from the decomposition of TFP growth were less than 4 % of the average TFP growth
rate. A dynamic diagram is presented of the source of TFP growth in China’s LMIE during
1996–2002 (Fig. 1).
For comparison, let the growth rate of TFP, FTP, change rate of TE, and contribution
to TFP growth by AE and SE divided by the TFP annual average growth rate 6.8 %.
The diagram clearly shows the great contribution to productivity growth by FTP and
the negative effect on productivity growth due to the slow technical progress in the lag-
ging enterprises. These two factors each have the magnitude that is equal to, or even
more than the annual average growth rate of TFP. By comparison, in terms of magni-
tude the macro net effect on productivity growth of AE and SE was negligible.
Fig. 1 Dynamic diagram of the source of TFP growth in China’s LMIE during 1996–2002 (Unit: taking theaverage Annual TFP Growth during 1996–2002 as 100 %)
Zhengge and Renjun Journal of Chinese Studies (2016) 1:2 Page 16 of 16
ConclusionsAt the turn of the century, China has experienced an industrial productivity revolution
mainly driven by the FTP. The FTP promoted the fast growth of industrial productivity
on one hand and broadened the TE gap on the other hand, which means that the sus-
tainable development of the economy and the industrial productivity revolution can be
driven by both making industrial frontier technology progress and increasing industrial
average efficiency. From the policy aspect, China’s industrial development must place
emphasis on enforcing competition, narrowing the efficiency gap between enterprises
and keeping the frontier technology progressing by emulating, learning from, catching
up with, helping each other, and at the same time absorbing advanced technology, and
keeping the frontier technology progressing. In addition, it is necessary to further the
market-orient economy reform, keeping the factor of labor and capital adjustable and
increase the AE and SE of industrial economy on the micro level.
AbbreviationsAE: Allocative efficiency; FTP: Frontier technical progress; GDP: Gross Domestic Product; LMIE: Large and medium-sizedindustrial enterprises; LR: Likelihood ratio; NPL: Non-performing loan; OECD: Organization for Economic Co-operationand Development; SE: Scale economy; TE: Technical efficiency; TFP: Total factor productivity
AcknowledgementThis study has been sponsored by National Fund for Social Sciences (No. 16BJY062, 16ZD006) and New Century TalentSupport Program of the Ministry of Education (NCET-10-0409).
Authors’ contributionsTUZ designed the investigation, analyzed the data and draft the manuscript; SR helped collected data and revise themanuscript. Both authors read and approved the final manuscript.
Competing interestsThe authors declare that they have no competing interest.
Received: 5 May 2016 Accepted: 21 August 2016
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